This paper presents an empirical investigation to study the effect of market management using Markowitz theorem. The study uses the information of 50 best performers on Tehran Stock Exchange over the period 2006-2009 and, using Markowitz theorem, the efficient asset allocation are determined and the result are analyzed. The proposed model of this paper has been solved using genetic algorithm. The results indicate that Tehran Stock Exchange has managed to perform much better than average world market in most years of studies especially on year 2009. The results of our investigation have also indicated that one could reach outstanding results using GA and forming efficient portfolio.

Root cause failure analysis (RCFA) is a structured process to acknowledge cause and effect relationship of failure or unfavorable events in the organization in order to prevent repetition or reduction of failures. There are various tools for RCFA like interviewing, fault tree analysis (FTA), 5 whys, failure mode and effects analysis (FMEA), Pareto analysis and storytelling method with different strengths and weaknesses. In this paper, an integrated process model is developed using Reality Charting, FMEA and DEMATEL to understand and implement RCFA effectively. The proposed process model has eight main steps. The presented model in a case study for the UTD-20 engine is implemented and thus, in addition to the use of supporting research in model development, real data as well as the approval of the UTD-20 analysis team members are assisted to validate the proposed model.

Ranking the companies can be a useful guide for investors to select an optimum portfolio. Tehran stock exchange (TSE) uses liquidity criterion to rank the companies; however, this study shows that preferences of investors, the criteria they use to evaluate companies’ performances, and the extent to which ranking of companies based on investors’ criteria are in line with the ranking announced by the stock exchange. Since the criteria used for ranking the companies are various and often conflicting and because each multiple criteria technique has its own specific characteristics, various rankings are offered. Therefore, it is required to utilize multiple criteria decision making models to avoid confusion of investors. For this purpose, some companies were selected from 50 top companies listed in 2011 in TSE, which maintained the reliability of their ranks and finally, 20 companies were selected and were ranked based on investors’ criteria using EECTRE III Technique. The obtained ranking was then compared with the ranking offered by stock exchange. Research results indicate that ELECTRE III technique was a useful and efficient method to select a portfolio. Moreover, value-based criteria as well as accounting criteria are suitable and useful bases for investors to select a portfolio.

In the present study, to achieve a comprehensive view of pressure conditions in the Black Sea region, sea level pressure data in Reanalysis II database in National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) were used. The data temporal resolution is daily and the spatial resolution is 2.5×2.5 degrees of arc. The investigated framework covers the areas between -30-70 degrees east longitude and 20-70 degrees north latitude and has dimensions equal 41×21 pixels consisting of 861 pixels. The studied period of time is 33-year (1979 to 2012) and includes 12419 days and 861 spatial pixels. Therefore, sea level pressure data matrix is 12419×861, which has 12419 temporal pixels and 861 spatial ones. In other words, the arrangement of the data is S-shaped. The rows of the matrix represent time while columns represent space. At the end, using the cluster analysis, six sea level pressure circulation patterns based on the spatial - temporal features were obtained: Red Sea trough is fall pattern; Iraq trough is spring pattern; Persian Gulf trough is transitional pattern; Persian Gulf strong trough is summer pattern; Caucasian high pressure is fall - winter pattern and the Caucasian Strong high pressure is winter pattern. In most of these patterns, intensive allobaric conditions can be observed in the troughs and ridges.

In this paper, we develop a novel framework for defining radial measures of centrality in complex networks. This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics. It is always an important issue to detect communities in complex networks as efficiently as possible to understand both the structure and function of the networks and to interpret radial centrality measures. Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures. Based on the proposed framework, an algorithm to compute the closeness centrality in complex networks is developed. We test the proposed algorithm on Zachary’s karate club network, which is considerably used as a benchmark for community detection in a network. The preliminary results indicate that the new method is efficient at detecting both good inter-cluster closeness centrality and the appropriate number of clusters.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

In the present paper, a novel intuitionistic fuzzy Multiple Attribute Decision Making (MADM) is proposed for modelling and solving analytical hierarchy process (AHP) problems with small amount of relationship among various criteria. Assigning a membership degree, fuzzy sets can model some uncertainty to the decision space. Intuitionistic fuzzy sets model the uncertainty more accurately associated with both membership and non-membership degree. Based on advantages of Intuitionistic fuzzy sets, this paper first uses IF-AHP to evaluate the weighting for each criterion and then develops an intuitionistic fuzzy DEMATEL method to establish contextual relationships among those criteria. Finally, an integrated IF-DEMATEL-AHP method is proposed and used for a case study for selecting managers in the automobile industry in Iran.

This paper presents an empirical investigation to study the relationship between bank debt guarantee and profitability, increase in banks resources and customer retention. The proposed study has been implemented in one of Iranian banks located in city of Zanjan, Iran over the period of 10 years. The study has used Pearson correlation test to examine three hypotheses of the survey. The results indicate that there were positive and meaningful relationship between bank debt guarantee and profitability (r = 0.863, P-value = 0.001), a positive relationship between bank debt guarantee and bank resources (r = 0.708 with P-value = 0.015) and there was a positive and meaningful relationship between bank debt guarantee and customer retention (r = 0.252 with P-value = 0.001).

By everyday development of storage and communicational and electronic media, there are significant amount of information being collected and stored in different forms such as electronic documents and document databases makes it difficult to process them, properly. To extract knowledge from this large volume of documental data, we require the use of documents organizing and indexing methods. Among these methods, we can consider clustering and classification methods where the objective is to organize documents and to increase the speed of accessing to required information. In most of document clustering methods, the clustering is mostly executed based on word frequency and considering document as a bag of words. In this essay, in order to decrease the number of features and to choose basic document feature, we use background knowledge and word clustering methods. In fact by using WordNet ontology, background knowledge and clustering method, the similar words of documents are clustered and the clusters with the number of words more than threshold are chosen and then their frequency of words is accepted as the effective features of document. The results of this proposed method simulation shows that the documents dimensions are decreased effectively and consequently the performance of documents clustering is increased.

This paper deals with a deterministic inventory model developed for deteriorating items having two separate storage facilities (owned and rented warehouses) due to limited capacity of the existing storage (owned warehouse) with linear time dependent demand (increasing) over a fixed finite time horizon. The model is formulated with infinite replenishment and the successive replenishment cycle lengths are in arithmetic progression. Partially backlogged shortages are allowed. The stocks of rented warehouse (RW) are transported to the owned warehouse (OW) in continuous release pattern. For this purpose, the model is formulated as a constrained non-linear mixed integer programming problem. For solving the problem, an advanced genetic algorithm (GA) has been developed. This advanced GA is based on ranking selection, elitism, whole arithmetic crossover and non-uniform mutation dependent on the age of the population. Our objective is to determine the optimal replenishment number, lot-size of two-warehouses (OW and RW) by maximizing the profit function. The model is illustrated with four numerical examples and sensitivity analyses of the optimal solution are performed with respect to different parameters.